This disclosure pertains generally to calibration and in particular pertains to a method and system for spectral calibration of a remote sensing sensor and a synthetic target having a tunable spectral composition.
Airborne, and space-borne remote sensing systems for geospatial survey, observation, target recognition, and other applications are increasing in use. For example, the remote sensing systems may be employed to detect anthropogenic and natural effects in climate change, geologic morphology and chemistry, hydrology, vegetation health, as well as to identify and distinguish between friend and foe military targets (target recognition), drug operations, terrorist activities, damage assessment, and emergency routes for responding to natural disasters.
In order for the remote sensing system to operate as intended to quantify physical properties of an observed object, image data obtained by the remote sensing system and the physical properties of the observed object are quantitatively linked. Thus, providers of remote sensing systems strive to provide adequate methods for addressing stability and accuracy requirements imposed by the user community to define and validate sensor spectral, spatial, and radiometric performance, and in turn establish the level of confidence for data exploitation.
Development of monochromatic and multispectral sensing systems continue to move toward increasing spatial resolution in response to the fact that most targets of interest are contained in only a few pixels or even sub-pixel (i.e., an image area of the target is less than a pixel area). Generally, each image is composed of a plurality of pixels, with the radiation sensed by each pixel analyzed to determine the physical content and make up of the target contained in a pixel area. However, for small targets, blur spots due to optical diffraction, electronic readout, sensor motion, atmospheric scattering, or any combination thereof, as well as other potential natural phenomena or technical issues, can smear light into nearby pixels spatially disconnected from the target and thus blur the image of the object. Multispectral and hyperspectral sensors collect image data across a plurality (e.g., tens to hundreds) of spectral bands in which the blurring or smearing effect can vary with wavelength.
As a result, knowledge of the spatial performance (i.e., sensor point spread function) is applied as part of a calibration process so as to achieve effective small targets. Hence, one element in the operation of airborne and space-borne imaging systems is sensor calibration on the ground before launch or flight. However, physical conditions within the imaging system or conditions in the atmosphere between the imaging system and the desired target or object may change from the calibration laboratory setting in such a way so as to skew the calibration values. Therefore, the sensor calibration on the ground in the laboratory becomes suspect until validated after deployment of the sensor. The validation of the calibration after sensor deployment or vicarious calibration of the sensor provides an absolute calibration of the sensor to ensure validity of the laboratory or ground based calibration or to correct the laboratory calibration to take into account conditions that may have occurred after deployment of the sensor.
Current vicarious calibration methods generally involve large surfaces of known reflectance, either natural targets (desert dry lake bed playa or uniform grass fields) or man-made (tarps or diffuse panels) targets. Natural targets have an unstable reflectance with significant bi-directional effects and generally provide only one light flux level for each calibration collection. Man-made diffuse reflectance targets provide better control of reflectance properties and multiple flux levels but, in order to be useful, must still be large, filling many pixels (typically on the scale of twenty to fifty meters or more). Man-made diffuse reflectance targets can be cumbersome to set out requiring an extensive support team for deployment and maintenance. In addition, both techniques require a broad range of ground truth measurements that characterize target and atmospheric optical properties at the time of the overpass for radiometric calibration. Furthermore, conventional vicarious calibration methods and systems are limited to calibrating radiometric properties of airborne, and space born remote sensing systems. These conventional vicarious methods and systems do not take into account the spectral dimension of the target since the calibration targets are generally spectrally flat. In addition, in these conventional vicarious methods, it is assumed that the spectral response function of the sensor is accurately known, and there is no significant spectral mixing with the background, both of which may be untrue.
Once calibrated, the performance of the sensor in detecting targets of interest through exploitation of their spectral properties generally requires validation. Effective validation would include looking at targets under a range of background contrasts, environmental settings, and atmospheric conditions. In addition, validation against targets that are difficult to reproduce, dangerous to handle or sensitive to national security create significant challenges in a effort to deploy for validation, all of which can be cost and effort inhibitive. Thus, it is desirable to provide the capability to put in their place surrogate targets with identical spectral properties, but those that are easy to deploy, which has many benefits.
Hence, there is a need in the art for a system and method of spectral calibration and validation of remote sensing systems and there is a need in the art for synthetic targets having tunable spectral composition.